{"cells":[{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"3ABE774BBF48480DA67F85B62DE17F85","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"# <center>Lecture 4:  Balance and Sequentiality in Bayesian Analyses  </center>  \n## <center>Instructor: Dr. Hu Chuan-Peng  </center>"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"4461DBF0C3A14F6D942929EAF68673EE","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"## 🔍 回顾 Bayes' Rule  \n\n在前面的课程中，我们使用几个简单的情况来帮助大家建立关于贝叶斯推断的直觉，这些情境包括单一事件、离散变量和连续变量。  \n\n这些情境可以总结如下：  \n\n| 知识点         | 内容描述                                         | 先验                   |   似然                          | 贝叶斯更新                     |  \n|---------------|------------------------------------------------|------------------------|--------------------------------|------------------------------|  \n| **单个事件**    | 一个使用特定语言风格的心理学实验被成功重复出来的可能性  | [OSC2015](https://doi.org/10.1126/science.aac4716)的结果           |   [Herzenstein et al 2024](https://doi.org/10.1177/09567976241254037 )年的数据    | 可视化的方式 + 简单计算         |  \n| **离散变量**       | 多次试验(多次进行重复实验)的成功率                  | 人为分配的三种成功率(0.2, 0.5, 0.8)和它们出现的可能性  | 进行重复后的结果在**三种**成功率下出现的可能性 | 简单的手动计算 |  \n| **连续变量**       | 多次试验(多次进行重复实验)的正确率                  | 符合正确率(0~1)数据特点和先验经验的概率分布| 进行重复后的结果在**所有**成功率/正确率下出现的可能性 | 已被证明的统计学公式|"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"0166A654BA4B485A8F8B227309E7A549","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"**所以，我们学习到了什么？**  \n1. 贝叶斯更新本质上类似于计数，符合人类推理的直觉；  \n2. 贝叶斯统计的主要作用在于将上述的直觉进行数学化，从而帮助我们在更复杂的情境中解决问题。"},{"cell_type":"markdown","metadata":{"_id":"34CB6D98E05F4F65BA21D9D2F905E01E","id":"CEA3961CF52149A484F39088682789F4","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"回顾：与心理学数据的结合——**随机点运动任务(random dot task)**  \n\n- 随机点运动任务要求被试判断运动点的移动方向（例如，向左还是向右）。  \n- 根据正确率的数据，可以使用心理物理曲线描述运动强度（点的一致性百分比）与决策正确率之间的关系。  \n\n<center>  \n    <table>  \n            <tr>  \n                <td><img src=\"https://cdn.kesci.com/upload/sjwnyt1yq4.gif?imageView2/0/w/400/h/400\" alt=\"\"></td>  \n                <td><img src=\"https://cdn.kesci.com/upload/sjzjcgwjrm.png?imageView2/0/w/400/h/400\" alt=\"\"></td>  \n            </tr>  \n            <tr>  \n                <td>一致性60%</td>  \n                <td>正确率与任务难度</td>  \n            </tr>  \n    </table>  \n</center>  \n\n\n> Shooshtari, S. V., Sadrabadi, J. E., Azizi, Z., & Ebrahimpour, R. (2018). Confidence representation of perceptual decision by EEG and eye data in a random dot motion task. *Neuroscience*, 406, 510–527. https://doi.org/10.1016/j.neuroscience.2019.03.031  "},{"cell_type":"markdown","metadata":{"id":"4AE859A568E1497EA353DDA174A74875","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"我们通过一名被试的数据推断在判断运动方向上的能力。但实际情况可能比我们这个例子更复杂，表现在两个方面：  \n\n- 不同的研究中有不同的结果，导致**不同的先验**。  \n- 不同的被试/实验可能有不同的数据量；同一个被试，也可能在不同的时间点有**不同的数据**。  \n\n不同的先验或者不同的数据对贝叶斯推断有什么影响？  \n\n如下展示了不同被试在随机点任务中的正确率："},{"cell_type":"code","metadata":{"collapsed":false,"id":"C3F9E432F3DA4FB09808939CCA11C8EE","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"# 安装和加载包\noptions(repos = c(CRAN = \"https://mirrors.tuna.tsinghua.edu.cn/CRAN/\"))\nif (!requireNamespace('pacman', quietly = TRUE)) {\n  install.packages('pacman')\n}\npacman::p_load(\"tidyverse\",\"ggplot2\", \"dplyr\",\"gridExtra\",\"papaja\",\"patchwork\")\noptions(warn = -1)  # 抑制警告\n\n# 数据导入\ndata <- tryCatch({\n  read.csv('/home/mw/input/bayes3797/evans2020JExpPsycholLearn_exp1_full_data.csv') #平台路径\n}, error = function(e) {\n  read.csv('data/evans2020JExpPsycholLearn_exp1_full_data.csv') #本地路径\n})\n\ncat(\"被试数量：\", length(unique(data$subject)), \"\\n\")\ndata %>% \n  dplyr::group_by(subject) %>% \n  dplyr::summarise(mean_correct = mean(correct, na.rm = TRUE)) %>% \n  head(10) %>%\n  as.data.frame()","outputs":[{"output_type":"stream","name":"stdout","text":"被试数量： 57 \n"},{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 10 × 2</caption>\n<thead>\n\t<tr><th scope=col>subject</th><th scope=col>mean_correct</th></tr>\n\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>31727</td><td>0.6513098</td></tr>\n\t<tr><td>65359</td><td>0.7533156</td></tr>\n\t<tr><td>66670</td><td>0.6808722</td></tr>\n\t<tr><td>71329</td><td>0.6970173</td></tr>\n\t<tr><td>71737</td><td>0.8225108</td></tr>\n\t<tr><td>75445</td><td>0.6686131</td></tr>\n\t<tr><td>77704</td><td>0.6115352</td></tr>\n\t<tr><td>77845</td><td>0.6754850</td></tr>\n\t<tr><td>79861</td><td>0.7652916</td></tr>\n\t<tr><td>80035</td><td>0.6675462</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 10 × 2\n\n| subject &lt;int&gt; | mean_correct &lt;dbl&gt; |\n|---|---|\n| 31727 | 0.6513098 |\n| 65359 | 0.7533156 |\n| 66670 | 0.6808722 |\n| 71329 | 0.6970173 |\n| 71737 | 0.8225108 |\n| 75445 | 0.6686131 |\n| 77704 | 0.6115352 |\n| 77845 | 0.6754850 |\n| 79861 | 0.7652916 |\n| 80035 | 0.6675462 |\n\n","text/latex":"A data.frame: 10 × 2\n\\begin{tabular}{ll}\n subject & mean\\_correct\\\\\n <int> & <dbl>\\\\\n\\hline\n\t 31727 & 0.6513098\\\\\n\t 65359 & 0.7533156\\\\\n\t 66670 & 0.6808722\\\\\n\t 71329 & 0.6970173\\\\\n\t 71737 & 0.8225108\\\\\n\t 75445 & 0.6686131\\\\\n\t 77704 & 0.6115352\\\\\n\t 77845 & 0.6754850\\\\\n\t 79861 & 0.7652916\\\\\n\t 80035 & 0.6675462\\\\\n\\end{tabular}\n","text/plain":"   subject mean_correct\n1  31727   0.6513098   \n2  65359   0.7533156   \n3  66670   0.6808722   \n4  71329   0.6970173   \n5  71737   0.8225108   \n6  75445   0.6686131   \n7  77704   0.6115352   \n8  77845   0.6754850   \n9  79861   0.7652916   \n10 80035   0.6675462   "},"metadata":{}}],"execution_count":1},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"2D7332380F3F4B2DB4CCC995EE4EE105","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"**本课目标：进一步强化大家关于贝叶斯推断的直觉**  \n- 数据在贝叶斯推断中的作用；  \n- 先验在贝叶斯推断中的作用。  \n\n**课程内容**  \n- Different data, different posteriors  \n  - Beta-Binomial 组合   \n  - Sequential analysis: Evolving with data  \n      - 随着数据到来的后验演进过程  \n      - 随着数据的影响，后验如何演变？  \n      - 特性1: 序列不变性  \n      - 特性2: 累积数据依赖性  \n  - 补充：贝叶斯序列分析的两大特征的数学证明  \n    - 数据序列不变性  \n    - 证明  \n    - 一次性观察数据 vs 序列观察数据  \n- Different priors, different posteriors  \n  - 极端先验  \n  - 贝叶斯的主观性  \n- Striking a balance between the prior & data  \n- 代码练习  \n  - 💐Bonus：使用数学公式证明，后验确实利用了来自先验和似然的信息  "},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"519CC0C84F1F4A0D9D0DD144054DEA08","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"## 数据不同时, 后验就会不同"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"EB87D97DBF2E4ECCA236DCFB6451D926","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"仍然以上节课的随机点运动任务下的数据为例。"},{"cell_type":"code","metadata":{"collapsed":false,"id":"002B90D90E6B4EE7865392F7A5B99D27","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"fragment"},"tags":[],"trusted":true},"source":"# 筛选符合条件的数据\ndata_subj1 <- data %>%\n  dplyr::select(subject, percentCoherence, correct, RT) %>%\n  dplyr::filter(subject == 82111, percentCoherence == 5)\n\n# 打印该被试在该条件下的平均正确率\ncat(\"被试 82111 在 5% 一致性正确率数据：\", mean(data_subj1$correct, na.rm = TRUE), \"\\n\")\nhead(data_subj1, 5)","outputs":[{"output_type":"stream","name":"stdout","text":"被试 82111 在 5% 一致性正确率数据： 0.6007905 \n"},{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 5 × 4</caption>\n<thead>\n\t<tr><th></th><th scope=col>subject</th><th scope=col>percentCoherence</th><th scope=col>correct</th><th scope=col>RT</th></tr>\n\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>1</th><td>82111</td><td>5</td><td>1</td><td>1978</td></tr>\n\t<tr><th scope=row>2</th><td>82111</td><td>5</td><td>1</td><td>1492</td></tr>\n\t<tr><th scope=row>3</th><td>82111</td><td>5</td><td>1</td><td>1262</td></tr>\n\t<tr><th scope=row>4</th><td>82111</td><td>5</td><td>0</td><td>4318</td></tr>\n\t<tr><th scope=row>5</th><td>82111</td><td>5</td><td>0</td><td>4514</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 5 × 4\n\n| <!--/--> | subject &lt;int&gt; | percentCoherence &lt;int&gt; | correct &lt;int&gt; | RT &lt;int&gt; |\n|---|---|---|---|---|\n| 1 | 82111 | 5 | 1 | 1978 |\n| 2 | 82111 | 5 | 1 | 1492 |\n| 3 | 82111 | 5 | 1 | 1262 |\n| 4 | 82111 | 5 | 0 | 4318 |\n| 5 | 82111 | 5 | 0 | 4514 |\n\n","text/latex":"A data.frame: 5 × 4\n\\begin{tabular}{r|llll}\n  & subject & percentCoherence & correct & RT\\\\\n  & <int> & <int> & <int> & <int>\\\\\n\\hline\n\t1 & 82111 & 5 & 1 & 1978\\\\\n\t2 & 82111 & 5 & 1 & 1492\\\\\n\t3 & 82111 & 5 & 1 & 1262\\\\\n\t4 & 82111 & 5 & 0 & 4318\\\\\n\t5 & 82111 & 5 & 0 & 4514\\\\\n\\end{tabular}\n","text/plain":"  subject percentCoherence correct RT  \n1 82111   5                1       1978\n2 82111   5                1       1492\n3 82111   5                1       1262\n4 82111   5                0       4318\n5 82111   5                0       4514"},"metadata":{}}],"execution_count":3},{"cell_type":"markdown","metadata":{"id":"21D3E59A98D74E39B392BD21E36DEF92","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"我们可以看到编号为“82111”的被试在253个试次中有152个试次判断为正确，5% 一致性的条件下的正确率约等于60%。"},{"cell_type":"code","metadata":{"collapsed":false,"id":"D916F474254248DD8D6EE7BAF4F39A3C","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"#统计 'binary' 列中各个值的出现次数\ndata_subj1 %>%\n  dplyr::count(correct)","outputs":[{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 2 × 2</caption>\n<thead>\n\t<tr><th scope=col>correct</th><th scope=col>n</th></tr>\n\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>0</td><td>101</td></tr>\n\t<tr><td>1</td><td>152</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 2 × 2\n\n| correct &lt;int&gt; | n &lt;int&gt; |\n|---|---|\n| 0 | 101 |\n| 1 | 152 |\n\n","text/latex":"A data.frame: 2 × 2\n\\begin{tabular}{ll}\n correct & n\\\\\n <int> & <int>\\\\\n\\hline\n\t 0 & 101\\\\\n\t 1 & 152\\\\\n\\end{tabular}\n","text/plain":"  correct n  \n1 0       101\n2 1       152"},"metadata":{}}],"execution_count":4},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"88ED56C86F0D4EB9AC6C0716D7992F20","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"### 回顾： Beta-Binomial模型  \n\n我们的先验分布为$Beta(70, 30)$，那么编号为“82111”的被试在253个试次中，有152次判断为正确的可能性（这节课中我们假设正确率为 $\\pi$ ），这个似然函数可以用**二项分布**来表示：  \n\n$$  \nY | \\pi  \\sim \\text{Bin}(n, \\pi)  \n$$  \n\n似然函数为：  \n\n$$  \nf(y|\\pi) = P(Y=y | \\pi) = \\binom{253}{152} \\pi^{152} (1-\\pi)^{101}  \n$$  \n"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"B965EAC0D9804430A42C14B12DFD7DC8","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"与上节课相同，我们的正确率 $\\pi$ 服从$Beta$ 分布，似然函数服从**二项分布**：  \n\n$$  \nY | \\pi  \\sim \\text{Bin}(n, \\, \\pi)  \n$$  \n\n$$  \n\\pi  \\sim \\text{Beta}(\\alpha, \\, \\beta)  \n$$  \n\n从上一节课我们知道，在这种情况下，**后验分布**仍然是 $Beta$ 分布，并且可以表示为：  \n\n$$  \n\\pi | (Y = y) \\sim \\text{Beta}(\\alpha + y, \\, \\beta + n - y)  \n$$  \n\n因此，编号为“82111”的被试在253个试次下，有152次判断为正确的情况时，后验分布可以写为：  \n\n$$  \n\\pi | (Y = 152) \\sim \\text{Beta}(\\alpha + 152, \\, \\beta + 101)  \n$$  "},{"cell_type":"code","metadata":{"collapsed":false,"id":"9B413B4D02284771854EDFC7042217DF","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"bayesian_analysis_plot <- function(\n    alpha, beta, y, n,\n    plot_prior      = TRUE,\n    plot_likelihood = TRUE,\n    plot_posterior  = TRUE,\n    xlabel          = expression(\"ACC\" ~ pi),\n    show_legend     = TRUE\n) {\n  \n  # 图例位置\n  legend_loc <- c(0.005, 0.995)  # 左上角\n  \n  # 先验分布\n  if (plot_prior) {\n    x_prior <- seq(qbeta(1e-4, alpha, beta), qbeta(1 - 1e-4, alpha, beta), length.out = 100)\n    y_prior <- dbeta(x_prior, alpha, beta)\n    prior_df <- data.frame(x = x_prior, y = y_prior, dist = \"Prior\")\n  }\n  \n  # 似然分布\n  if (plot_likelihood) {\n    x_like <- seq(0, 1, length.out = 1000)\n    y_like <- dbeta(x_like, y + 1, n - y + 1)\n    like_df <- data.frame(x = x_like, y = y_like, dist = \"Likelihood\")\n  }\n  \n  # 后验分布\n  if (plot_posterior) {\n    a_post <- alpha + y\n    b_post <- beta + n - y\n    x_post <- seq(qbeta(1e-4, a_post, b_post), qbeta(1 - 1e-4, a_post, b_post), length.out = 100)\n    y_post <- dbeta(x_post, a_post, b_post)\n    post_df <- data.frame(x = x_post, y = y_post, dist = \"Posterior\")\n  }\n  \n  # 合并数据 \n  plot_data <- do.call(rbind, Filter(Negate(is.null), list(\n    if (plot_prior)      transform(prior_df, dist = \"prior\")      else NULL,\n    if (plot_likelihood) transform(like_df,  dist = \"likelihood\") else NULL,\n    if (plot_posterior)  transform(post_df,  dist = \"posterior\")  else NULL\n  )))\n  plot_data$dist <- factor(plot_data$dist, c(\"prior\",\"likelihood\",\"posterior\"))\n  \n  # 作图\n  cols_fill <- c(prior=\"#f0e442\", likelihood=\"#0071b2\", posterior=\"#009e74\")\n  present   <- intersect(c(\"prior\",\"likelihood\",\"posterior\"), unique(plot_data$dist))\n  legend_levels <- as.vector(rbind(paste0(present, \"_line\"),\n                                   paste0(present, \"_fill\")))\n\n  labs_all <- setNames(c(\n    sprintf(\"Beta(alpha=%d, beta=%d)\", alpha, beta),          \"Prior\",\n    sprintf(\"Binomial(n=%d, p=%.2g)\", n, y/n),                \"Likelihood\",\n    sprintf(\"Beta(alpha=%d, beta=%d)\", alpha+y, beta+n-y),    \"Posterior\"\n  ), c(\"prior_line\",\"prior_fill\",\"likelihood_line\",\"likelihood_fill\",\n       \"posterior_line\",\"posterior_fill\"))[legend_levels]\n\n  plot_data$legend_line <- factor(paste0(plot_data$dist, \"_line\"), levels = legend_levels)\n  plot_data$legend_fill <- factor(paste0(plot_data$dist, \"_fill\"), levels = legend_levels)\n\n  p <- ggplot2::ggplot(plot_data, ggplot2::aes(x, y)) +\n    ggplot2::geom_area(ggplot2::aes(fill  = legend_fill),\n                       alpha = 0.7, position = \"identity\", colour = NA) +\n    ggplot2::geom_line(ggplot2::aes(color = legend_line), linewidth = 1.2, lineend = \"round\", linejoin = \"round\") +\n    ggplot2::labs(x = xlabel, y = \"Density\") +\n    ggplot2::scale_y_continuous(expand = c(0, 0)) +\n    \n    ggplot2::scale_fill_manual(\n      name   = NULL,\n      values = setNames(ifelse(grepl(\"_fill$\", legend_levels),\n                               cols_fill[sub(\"_fill$\", \"\", legend_levels)], NA),\n                        legend_levels),\n      breaks = legend_levels,\n      labels = labs_all,\n      drop   = FALSE\n    ) +\n    ggplot2::scale_color_manual(\n      name   = NULL,\n      values = setNames(ifelse(grepl(\"_line$\", legend_levels), \"black\", NA),\n                        legend_levels),\n      breaks = legend_levels,\n      labels = labs_all,\n      drop   = FALSE\n    ) +\n    ggplot2::guides(\n      fill  = ggplot2::guide_legend(order = 1, byrow = TRUE),\n      color = ggplot2::guide_legend(order = 1, byrow = TRUE)\n    ) +\n    papaja::theme_apa()+ \n    ggplot2::theme(\n      legend.margin = margin(t = 2, r = 4, b = 2, l = 4, unit = \"pt\"),\n      legend.background    = element_rect(fill = \"transparent\", colour = \"NA\", linewidth = 0.6),\n      legend.box.background= element_rect(fill = \"transparent\", colour = \"grey\", linewidth = 0.6),\n      legend.key           = element_rect(fill = \"transparent\", colour = NA),\n      axis.text.y  = ggplot2::element_blank(),\n      axis.ticks.y = ggplot2::element_blank(),\n      axis.title.y = ggplot2::element_blank(),\n      axis.text.x  = ggplot2::element_text(size = 12), \n      legend.box = \"vertical\",\n      legend.position      = legend_loc ,\n      legend.justification = c(0, 1) \n    )\n  \n  return(p)\n}","outputs":[],"execution_count":5},{"cell_type":"code","metadata":{"collapsed":false,"id":"FC0D4BC0D19F434A91F8D575144C2427","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"options(repr.plot.width=10,repr.plot.height=5)\nfig <- bayesian_analysis_plot(70, 30, 152, 253) +\n  ggplot2::coord_cartesian(xlim = c(0.4, 0.9))  \nprint(fig)","outputs":[{"output_type":"display_data","data":{"text/plain":"plot without 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HUgsgLj+QODrfL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\nZG9iagpzdGFydHhyZWYKMzE5MTYKJSVFT0YK","text/html":"<img src=\"https://cdn.kesci.com/upload/rt/FC0D4BC0D19F434A91F8D575144C2427/t3507tefxn.svg\">"},"metadata":{"image/png":{"width":600,"height":300},"image/jpeg":{"width":600,"height":300},"image/svg+xml":{"width":600,"height":300,"isolated":true},"application/pdf":{"width":600,"height":300}}}],"execution_count":6},{"cell_type":"markdown","metadata":{"id":"29CFE931B66646FDBDADE73C476929DA","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"在看过单个被试的后验分布后，你或许会想，不同输入数据下后验分布有何区别？  \n\n通过贝叶斯公式我们知道，后验的分布主要取决于先验与似然；  \n\n--> **不同的数据意味着不同的似然**  \n\n$$  \nf(\\pi | y) = \\frac{f(\\pi)L(\\pi|y)}{f(y)} \\propto f(\\pi)L(\\pi|y)  \n$$  \n\n接下来我们将看到，不同的似然对后验分布的影响。  "},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"04DC00C10A924895B949EC077DBA0A9E","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"### 不同数据条件下，似然对于后验的影响  \n\n注意：这里的“不同数据”指的是这些数据具有类似的模式，仅仅在**数据量**的不同。在这种情况下贝叶斯推断会受到什么样的影响？  \n\n假定我们重新选择一位被试，该被试的编号为31727，且该被试完成了三个不同的实验区块（block）。  \n- 为便于表示，将被试在3个block中的数据结果分别命名为a、b、c。  \n\n每次block的表现如下：  \n\n- 第1个 block（a）：进行了 128 次试次，其中有 77 次（60%）判断为正确。  \n- 前2个 block（a + b）：进行了 254 次试次，其中有 152 次（60%）判断为正确。  \n- 全部3个 block（a + b + c）：进行了 385 次试次，其中有 231 次（60%）判断为正确。  \n\n可以看出, 这个被试的表现相当稳。  \n\n问题：你认为以上三种情况下，我们得到的后验会不会有不同，为什么？以及，如果我们使用经典统计的看会有什么结果？"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"7AA058CD0FCF4F63A4D503D2323053E1","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"将**先验信念**仍然设定为$Beta(70, \\, 30)$。  \n  \n我们将使用被试（`31727`）的观测数据，分析不同的似然函数，以及它们是如何影响每个block下后验分布的更新。"},{"cell_type":"markdown","metadata":{"id":"57AAB21ABBA747108C6756DBD62CB952","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"我们可以把三种似然函数写出来：  \n\n**第1个 block的情况**:  \n\n在 a 的情况下（128次试次，77次判断为正确），似然函数为：  \n\n$$  \nf(y \\mid \\pi) = P(Y = 77 \\mid \\pi) = \\binom{128}{77} \\pi^{77} (1 - \\pi)^{128 - 77}  \n$$  \n\n即：  \n\n$$  \nf(y \\mid \\pi) = \\binom{128}{77} \\pi^{77} (1 - \\pi)^{51}  \n$$  \n\n**前2个 block的情况**:  \n\n在 a+b 的情况下（254次试次，152次判断为正确），似然函数为：  \n\n$$  \nf(y \\mid \\pi) = P(Y = 152 \\mid \\pi) = \\binom{254}{152} \\pi^{152} (1 - \\pi)^{254 - 152}  \n$$  \n\n即：  \n\n$$  \nf(y \\mid \\pi) = \\binom{254}{152} \\pi^{152} (1 - \\pi)^{102}  \n$$  \n\n**全部3个 block的情况**:  \n\n在 a+b+c 下（385次试次，231次判断为正确），似然函数为：  \n\n$$  \nf(y \\mid \\pi) = P(Y = 231 \\mid \\pi) = \\binom{385}{231} \\pi^{231} (1 - \\pi)^{385 - 231}  \n$$  \n\n即：  \n\n$$  \nf(y \\mid \\pi) = \\binom{385}{231} \\pi^{231} (1 - \\pi)^{154}  \n$$  "},{"cell_type":"code","metadata":{"collapsed":false,"id":"F899BEB72FC94437A2C8EFB3ADC58A84","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"# 定义先验分布的 alpha 和 beta\nalpha <- 70\nbeta  <- 30\np1 <- bayesian_analysis_plot(alpha, beta, y = 77,  n = 128, plot_posterior = FALSE) +\n  coord_cartesian(xlim = c(0.4, 0.9)) + \n  theme(       \n    legend.text       = element_text(size = 10),\n    #legend.key.height = grid::unit(6, \"pt\"),\n    #legend.key.width  = grid::unit(6, \"pt\"),\n    #legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np2 <- bayesian_analysis_plot(alpha, beta, y = 152, n = 254, plot_posterior = FALSE) +\n  coord_cartesian(xlim = c(0.4, 0.9))+ \n  theme(       \n    legend.text       = element_text(size = 10),\n    #legend.key.height = grid::unit(6, \"pt\"),\n    #legend.key.width  = grid::unit(6, \"pt\"),\n    #legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np3 <- bayesian_analysis_plot(alpha, beta, y = 231, n = 385, plot_posterior = FALSE) +\n  coord_cartesian(xlim = c(0.4, 0.9))+ \n  theme(       \n    legend.text       = element_text(size = 10),\n    #legend.key.height = grid::unit(6, \"pt\"),\n    #legend.key.width  = grid::unit(6, \"pt\"),\n    #legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np1 + p2 + p3 ","outputs":[{"output_type":"display_data","data":{"text/plain":"plot without 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1U+jJSvmbCywUi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src=\"https://cdn.kesci.com/upload/rt/F899BEB72FC94437A2C8EFB3ADC58A84/t3509qtt0x.svg\">"},"metadata":{"image/png":{"width":600,"height":300},"image/jpeg":{"width":600,"height":300},"image/svg+xml":{"width":600,"height":300,"isolated":true},"application/pdf":{"width":600,"height":300}}}],"execution_count":7},{"cell_type":"markdown","metadata":{"id":"2C5B2EBFAF0849C3B9FE62717852FB8D","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"🤔**思考时间**：  \n\n这些数据的“正确率”完全相同，仅仅是数据的**量**不同，后验分布会一样吗？？"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"43D813BB57244223A495A8A883CF0215","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"## 似然对后验的影响  \n\n先看图：虽然三种似然分布的均值都是 0.60，但随着样本量的增大，似然的分布变得越窄，反映的信息越集中。  "},{"cell_type":"code","metadata":{"jupyter":{},"collapsed":false,"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"2ADFE2B2A5F747859AB19E48903E84B9","notebookId":"68d5045de81133f9301bdd7b","trusted":true},"source":"# 分别创建三个图（包含后验）\np1 <- bayesian_analysis_plot(alpha, beta, y = 77,  n = 128, plot_posterior = TRUE) +\n  coord_cartesian(xlim = c(0.4, 0.9))+ \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(6, \"pt\"),\n    legend.key.width  = grid::unit(6, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np2 <- bayesian_analysis_plot(alpha, beta, y = 152, n = 254, plot_posterior = TRUE) +\n  coord_cartesian(xlim = c(0.4, 0.9))+ \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(6, \"pt\"),\n    legend.key.width  = grid::unit(6, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np3 <- bayesian_analysis_plot(alpha, beta, y = 231, n = 385, plot_posterior = TRUE) +\n  coord_cartesian(xlim = c(0.4, 0.9))+ \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(6, \"pt\"),\n    legend.key.width  = grid::unit(6, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np1 + p2 + p3","outputs":[{"output_type":"display_data","data":{"text/plain":"plot without 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TDsDYdwF8CGZPr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\nRgo=","text/html":"<img src=\"https://cdn.kesci.com/upload/rt/2ADFE2B2A5F747859AB19E48903E84B9/t350a2gp3c.svg\">"},"metadata":{"image/png":{"width":600,"height":300},"image/jpeg":{"width":600,"height":300},"image/svg+xml":{"width":600,"height":300,"isolated":true},"application/pdf":{"width":600,"height":300}}}],"execution_count":8},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"A1208BB56C814E1FB173AEAD6C27A5BA","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"*注意：在似然中，y轴为$f(y|\\pi)$，表示在特定的$\\pi$值下产生当前数据的相对可能性。*  \n\n- **1个 block的情况：进行了 128 次试次，其中有 77 次（60%）判断为正确**  \n  - 样本量较小，似然分布较宽，意味着观测数据对正确率$\\pi$的约束不强，反映出不确定性较高。  \n\n- **2个 block的情况：一共进行了 254 次试次，其中有 152 次（60%）判断为正确**  \n  - 随着样本量的增大，似然分布变得更窄，表示观测数据更加集中，推断出的正确率更加有约束力。  \n\n- **3个 block的情况：总计进行了 385 次试次，其中有 231 次（60%）判断为正确**  \n  - 样本量进一步增大，似然分布更加集中，数据提供了更强的约束，推断出的正确率更加精确。  \n\n\n- **结论**  \n  1. **当似然反映的信息越集中时，它对后验的影响越大**  \n  2. 样本量越大，似然对正确率$\\pi$的约束力越强，后验分布也会更加集中于观测结果，这使得我们对$\\pi$的推断更为确定。  "},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"EB6B9920F59B4E389B9C4DC436A45387","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"我们可以使用公式快速计算出后验$Beta$分布中的参数：  \n\n<center>  \n\n| Situations         | Data (y, n)  | Posterior                                 |  \n|-------------|------------------|------------------------------------------|  \n| **a**  | y = 77, n = 128  | Beta(70 + 77, 30 + 128 - 77) = Beta(147, 81)  |  \n| **a+b**  | y = 152, n = 254 | Beta(70 + 152, 30 + 254 - 152) = Beta(222, 132) |  \n| **a+b+c**  | y = 231, n = 385 | Beta(70 + 231, 30 + 385 - 231) = Beta(301, 184) |  \n\n</center>"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"8C318CFF221047818A740DE54B173542","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"### Sequential analysis: Evolving with data"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"29A73F3A548A497CB5DB0B373500A630","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"在贝叶斯框架中，随着更多数据的到来，数据对后验的影响逐渐增加。相对地，先验信念的影响逐渐减小。总体上，数据的增加使得我们对正确率 $\\pi$ 的推断更为精确。 "},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"CCF945AABA0B4CB3919CF70A85254D66","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"##### 随着数据的影响，后验如何演变？  \n\n在随机点运动任务的例子中，我们逐步观察到随着样本量的增加：  \n- **先验信念的影响逐渐减弱**：最初的先验分布提供了对正确率 $\\pi$ 的初步信念，但随着更多数据被观察到，先验对后验的影响逐渐减弱。  \n- **数据的主导地位逐渐增强**：随着实验试次的增多，似然函数变得越来越窄，表示观测数据对正确率的推断更加集中和精确。"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"8753A969AE1C49C5AFE506F8674B21B1","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"**序列贝叶斯分析 (Sequential Bayesian Analysis)**  \n\n在我们的例子中，随着被试的试次不断增加，我们的后验也在逐步更新，更新过程如下：  \n\n<center>  \n\n| 更新步骤         | Data (y, n)  | Model                                |  \n|-------------|------------------|------------------------------------------|  \n| **NA**  | NA               | Beta(70, 30 ) = Beta(70, 30)  |  \n| **a**  | y = 77, n = 128  | Beta(70 + 77, 30 + 128 - 77) = Beta(147, 81)  |  \n| **a+b**  | y = 152, n = 254 | Beta(70 + 152, 30 + 254 - 152) = Beta(222, 132) |  \n| **a+b+c**  | y = 231, n = 385 | Beta(70 + 231, 30 + 385 - 231) = Beta(301, 184) |  \n\n<center>  "},{"cell_type":"markdown","metadata":{"id":"B44C14045E9546B79A835006D6DE9174","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"**序列贝叶斯分析（又称贝叶斯学习）**  \n\n- 在序列贝叶斯分析中，随着新数据的到来，后验模型会逐步更新。  \n- 每一份新数据都会使前一次后验模型（反映我们在观察到这些数据之前的理解）成为新的先验模型。  \n  \n![Image Name](https://cdn.kesci.com/upload/skex9x185z.gif?imageView2/0/w/960/h/960)  \n"},{"cell_type":"code","metadata":{"collapsed":false,"hide_input":true,"id":"89838407635F4B259ABDE540F3DDB68B","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"skip"},"tags":[],"trusted":true},"source":"library(shiny)\n\ndata_correct <- data_subj1$correct\n\nui <- fluidPage(\n  tags$head(\n    tags$style(HTML(\"\n      .update-btn {\n        width: 200px; height: 30px; border-radius: 10px; \n        background-color: #1E90FF; color: white; font-size: 18px;\n        border: none;\n      }\n    \"))\n  ),\n  titlePanel(\"Bayesian Updating (Beta-Binomial)\"),\n  \n  fluidRow(\n    column(\n      width = 4,\n      actionButton(\"update\", \"Update with more data\", class = \"update-btn\"),\n      br(), br(),\n      sliderInput(\"prior_alpha\", \"Prior alpha\", min = 1, max = 200, value = 1, step = 1),\n      sliderInput(\"prior_beta\",  \"Prior beta\",  min = 1, max = 200, value = 1, step = 1),\n      sliderInput(\"init_trial\",  \"Initial trial (start index)\", min = 1, max = 100, value = 1, step = 1),\n      sliderInput(\"step\",        \"Step size (per update)\",      min = 1, max = 20,  value = 1,  step = 1),\n      checkboxInput(\"show_prior\",      \"Show prior\", TRUE),\n      checkboxInput(\"show_last_post\",  \"Show posterior (t-1)\", TRUE)\n    ),\n    column(\n      width = 8,\n      plotOutput(\"plt\", height = 480)\n    )\n  )\n)\n\nserver <- function(input, output, session) {\n  \n  count <- reactiveVal(-1)\n  \n  observeEvent(input$update, {\n    count(count() + 1)\n  })\n  \n  make_plot <- reactive({\n    prior_alpha   <- input$prior_alpha\n    prior_beta    <- input$prior_beta\n    init_trial    <- input$init_trial\n    step          <- input$step\n    show_prior    <- isTRUE(input$show_prior)\n    show_last_post<- isTRUE(input$show_last_post)\n    \n    cnt <- count()\n    \n    x <- seq(0, 1, length.out = 1000)\n    \n    trial_number_last    <- init_trial + (cnt - 1) * step\n    trial_number_current <- init_trial + cnt * step\n    \n    p <- ggplot() + theme_classic() +\n      theme(legend.title = element_blank(),\n            axis.title.y = ggplot2::element_blank(),\n            axis.title.x = ggplot2::element_blank(),\n            axis.text.x  = ggplot2::element_text(size = 12),\n            axis.text.y  = ggplot2::element_text(size = 12),\n            legend.text = element_text(size = 16))\n    \n    if (show_prior) {\n      y_prior <- dbeta(x, prior_alpha, prior_beta)\n      p <- p + geom_line(aes(x = x, y = y_prior, color = \"prior\"),\n                         linetype = \"dotdash\", linewidth = 0.9)\n    }\n    \n    n_total <- length(data_correct)\n    \n    if (cnt < 0) {\n      p <- p + ggtitle(sprintf(\"Prior Beta: alpha=%d, beta=%d\", prior_alpha, prior_beta)) +\n        theme(plot.title = element_text(hjust = 0.5))\n      return(\n        p + scale_color_manual(name = NULL,values = c(\"prior\" = \"navy\"))+\n          papaja::theme_apa()+\n          ggplot2::theme(\n            legend.margin = margin(t = 2, r = 4, b = 2, l = 4, unit = \"pt\"),\n            legend.background    = element_rect(fill = \"transparent\", colour = \"NA\", linewidth = 0.6),\n            legend.box.background= element_rect(fill = \"transparent\", colour = \"grey\", linewidth = 0.6),\n            legend.key           = element_rect(fill = \"transparent\", colour = NA),\n            axis.title.x = ggplot2::element_blank(),\n            axis.title.y = ggplot2::element_blank(),\n            legend.box = \"vertical\",\n          )\n      )\n    } else if (trial_number_current > n_total) {\n      p <- p + ggtitle(sprintf(\"All Trials %d with %d corrects\", n_total, sum(data_correct))) +\n        theme(plot.title = element_text(hjust = 0.5))\n      return(\n        p + scale_color_manual(name = NULL,values = c(\"prior\" = \"navy\",\n                                          \"posterior (t-1)\" = \"olivedrab\",\n                                          \"posterior\" = \"orangered\"))+\n          papaja::theme_apa()+\n          ggplot2::theme(\n            legend.margin = margin(t = 2, r = 4, b = 2, l = 4, unit = \"pt\"),\n            legend.background    = element_rect(fill = \"transparent\", colour = \"NA\", linewidth = 0.6),\n            legend.box.background= element_rect(fill = \"transparent\", colour = \"grey\", linewidth = 0.6),\n            legend.key           = element_rect(fill = \"transparent\", colour = NA),\n            axis.title.x = ggplot2::element_blank(),\n            axis.title.y = ggplot2::element_blank(),\n            legend.box = \"vertical\",\n          )\n      )\n    } else if (cnt == 0) {\n      tmp_data <- data_correct[seq_len(trial_number_current)]\n      p <- p + ggtitle(sprintf(\"Trial %d with %d trials and %d corrects\",\n                               trial_number_current - init_trial,\n                               length(tmp_data),\n                               sum(tmp_data))) +\n        theme(plot.title = element_text(hjust = 0.5))\n    } else if (cnt > 0) {\n      start_idx <- trial_number_last + 1\n      end_idx   <- trial_number_current\n      \n      if (start_idx <= end_idx && start_idx >= 1) {\n        tmp_data <- data_correct[start_idx:end_idx]\n      } else {\n        tmp_data <- integer(0)\n      }\n      \n      p <- p + ggtitle(sprintf(\"Trial %d with %d trials and %d corrects\",\n                               trial_number_last,\n                               length(tmp_data),\n                               sum(tmp_data))) +\n        theme(plot.title = element_text(hjust = 0.5))\n      \n      if (show_last_post && trial_number_last > 0) {\n        n_correct_last <- sum(data_correct[seq_len(trial_number_last)])\n        n_false_last   <- trial_number_last - n_correct_last\n        post_a_last    <- prior_alpha + n_correct_last\n        post_b_last    <- prior_beta  + n_false_last\n        y_last <- dbeta(x, post_a_last, post_b_last)\n        p <- p + geom_line(aes(x = x, y = y_last, color = \"posterior (t-1)\"),\n                           alpha = 0.4, linewidth = 0.9)\n      }\n    }\n    \n    n_correct_cur <- sum(data_correct[seq_len(trial_number_current)])\n    n_false_cur   <- trial_number_current - n_correct_cur\n    post_a_cur    <- prior_alpha + n_correct_cur\n    post_b_cur    <- prior_beta  + n_false_cur\n    y_cur <- dbeta(x, post_a_cur, post_b_cur)\n    p <- p + geom_line(aes(x = x, y = y_cur, color = \"posterior\"), linewidth = 1.1)\n    \n    p + scale_color_manual(name = NULL,values = c(\"prior\" = \"navy\",\n                                      \"posterior (t-1)\" = \"olivedrab\",\n                                      \"posterior\" = \"orangered\")) +\n\n      papaja::theme_apa()+\n      ggplot2::theme(\n        legend.margin = margin(t = 2, r = 4, b = 2, l = 4, unit = \"pt\"),\n        legend.background    = element_rect(fill = \"transparent\", colour = \"NA\", linewidth = 0.6),\n        legend.box.background= element_rect(fill = \"transparent\", colour = \"grey\", linewidth = 0.6),\n        legend.key           = element_rect(fill = \"transparent\", colour = NA),\n        axis.title.x = ggplot2::element_blank(),\n        axis.title.y = ggplot2::element_blank(),\n        legend.box = \"vertical\",\n      )\n  })\n  \n  output$plt <- renderPlot({\n    make_plot()\n  })\n}","outputs":[],"execution_count":12},{"cell_type":"code","metadata":{"slideshow":{"slide_type":"fragment"},"jupyter":{},"collapsed":false,"scrolled":false,"tags":[],"id":"6623D42567484B91BEB7BF93210A3081","notebookId":"68d5045de81133f9301bdd7b","hide_input":true,"trusted":true},"source":"shinyApp(ui, server)","outputs":[{"output_type":"stream","name":"stderr","text":"\nListening on http://0.0.0.0:7749\n\n"}],"execution_count":13},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"C0BB7AED4C7747EBB1852DEF2C4E7968","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"贝叶斯推断一个显著的特性是随着新数据的加入而演进。这种序列分析还有两个基本且符合常识的特点。  \n\n- **序列不变性**：后验分布不受数据输入的序列影响，只要数据总量相同，最终结果是一致的。  \n- **累积数据依赖性**：我们可以逐步或一次性评估数据，后验分布只依赖于观测数据的总量。  "},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"90B627C0884A4985B65561CA0A320FB9","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"\n##### 特性1: 序列不变性  \n\n- 在不同观测序列下的观测数据及对应的后验分布，尽管序列不同，最终结果将一致：  \n\n\n| 观测序列         | Data (y, n)  | Model                                |  \n|-------------|------------------|------------------------------------------|  \n| **NA**  | NA               | Beta(70, 30 ) = Beta(70, 30)  |  \n| **a**  | y = 77, n = 128  | Beta(70 + 77, 30 + 128 - 77) = Beta(147, 81)  |  \n| **a+b**  | y = 152, n = 254 | Beta(70 + 152, 30 + 254 - 152) = Beta(222, 132) |  \n| **a+b+c**  | y = 231, n = 385 | Beta(70 + 231, 30 + 385 - 231) = Beta(301, 184) |  \n\n- 为了更好地展示序列不变性，假设被试先从区块b开始进行实验，按照b、b+c和a+b+c的序列进行更新，更新的表格如下：  \n\n| 观测序列      | Data (y, n)      | Model                                 |  \n|---------------|------------------|---------------------------------------|  \n| **NA**   | NA               | Beta(70, 30) = Beta(70, 30)          |  \n| **b**         | y = 75, n = 126  | Beta(70 + 75, 30 + 126 - 75) = Beta(145, 81) |  \n| **b+c**       | y = 154, n = 257 | Beta(70 + 154, 30 + 257 - 154) = Beta(224, 133) |  \n| **a+b+c**     | y = 231, n = 385 | Beta(70 + 231, 30 + 385 - 231) = Beta(301, 184) |  \n\n无论采取哪种序列进行观测，最终的后验分布将基于总成功数和总试次数的合并计算。  \n"},{"cell_type":"code","metadata":{"jupyter":{},"collapsed":false,"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"A137BAC40CF94C6499B278C27DD764B0","notebookId":"68d5045de81133f9301bdd7b","trusted":true},"source":"# ----------------------------------------\n# ----------------------------------------","outputs":[],"execution_count":null},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"C0402A375E194D9E9149E67347809BEB","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"##### 特性2: 累积数据依赖性  \n\n- 例如，在这三次的随机点运动任务中，共有 $n = a + b + c = 385$ 次，其中 $Y = 231$ 次判断正确。  \n- 初始先验分布是 $\\text{Beta}(70, 30)$，通过累积数据可以直接计算出后验分布为：  \n\n$$  \n\\pi | (Y = 231) \\sim \\text{Beta}(70 + 231, 30 + 385 - 231) = \\text{Beta}(301, 184)  \n$$  \n\n\n根据累积数据依赖性，后验分布只依赖于观测数据的总量，而不关心观测的顺序，无论观测顺序如何，最终的后验分布都为$\\text{Beta}(530, 337)$."},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"6F2A44979B7C42BE91206E87A5E888A8","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"### 补充：贝叶斯序列分析的两大特征的数学证明(略)  \n\n在之前的讨论中，我们通过实例展示了**数据序列不变性**的特点。接下来，我们将为该特性在所有贝叶斯模型中的适用性进行数学证明。  \n\n#### 数据序列不变性  \n\n定义 $\\theta$ 为感兴趣的任意参数，其先验概率密度函数为 $f(\\theta)$。无论我们先观察数据点 $y_1$ 然后观察 $y_2$，还是先观察 $y_2$ 再观察 $y_1$，最终的后验分布都是相同的，即：  \n\n$$  \nf(\\theta \\mid y_1, y_2) = f(\\theta \\mid y_2, y_1)  \n$$  \n\n同样，无论我们一次性观察所有数据，还是按序列逐步观察数据，最终的后验分布都不受影响。  \n\n#### 证明  \n\n为了证明这一点，我们首先考虑通过序列观察数据 $y_1$ 和 $y_2$ 来构建的后验概率密度函数 $f(\\theta \\mid y_1, y_2)$。  \n在这个过程中，后验概率的演化可以分两步进行：  \n\n1. **第一步**：我们首先从原始先验分布 $f(\\theta)$ 和基于第一个数据点 $y_1$ 的似然函数 $L(\\theta \\mid y_1)$ 构建后验分布：  \n\n   $$  \n   f(\\theta \\mid y_1) = \\frac{f(\\theta) L(\\theta \\mid y_1)}{f(y_1)}  \n   $$  \n\n   其中，$f(y_1)$ 是归一化常数，用于确保后验分布的积分为 1。  \n\n2. **第二步**：在观察到新的数据 $y_2$ 后，我们使用 $f(\\theta \\mid y_1)$ 作为先验，并根据数据 $y_2$ 更新模型：  \n\n   $$  \n   f(\\theta \\mid y_2, y_1) = \\frac{f(\\theta \\mid y_1) L(\\theta \\mid y_2)}{f(y_2)}  \n   $$  \n\n   代入第一步中的 $f(\\theta \\mid y_1)$，得到：  \n\n   $$  \n   f(\\theta \\mid y_2, y_1) = \\frac{\\frac{f(\\theta) L(\\theta \\mid y_1)}{f(y_1)} L(\\theta \\mid y_2)}{f(y_2)}  \n   $$  \n\n   化简后：  \n\n   $$  \n   f(\\theta \\mid y_2, y_1) = \\frac{f(\\theta) L(\\theta \\mid y_1) L(\\theta \\mid y_2)}{f(y_1) f(y_2)}  \n   $$"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"64DC868308C3430098FEA4265ECD4940","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"类似地，以相反的顺序，先观察 $y_2$ 然后观察 $y_1$，我们得到同样的后验分布：  \n\n$$  \nf(\\theta \\mid y_1, y_2) = \\frac{f(\\theta) L(\\theta \\mid y_2) L(\\theta \\mid y_1)}{f(y_2) f(y_1)}  \n$$  \n\n因此，后验分布 $f(\\theta \\mid y_1, y_2)$ 与 $f(\\theta \\mid y_2, y_1)$ 相同，证明了数据的顺序不会影响最终的后验分布。"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"430224544C7848FF8773B74A5E937011","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"#### 一次性观察数据 vs 顺序观察数据  \n\n不仅数据的顺序不影响后验分布，观察数据的方式（一次性或逐步）也不影响最终结果。为此，假设我们从先验分布 $f(\\theta)$ 开始，并同时观察数据 $(y_1, y_2)$。假设这些数据点在无条件和有条件下是独立的，那么：  \n\n$$  \nf(y_1, y_2) = f(y_1) f(y_2)  \n\\quad \\text{和} \\quad  \nf(y_1, y_2 \\mid \\theta) = f(y_1 \\mid \\theta) f(y_2 \\mid \\theta)  \n$$  \n\n因此，从同时观察数据 $(y_1, y_2)$ 得到的后验分布为：  \n\n$$  \nf(\\theta \\mid y_1, y_2) = \\frac{f(\\theta) f(y_1, y_2 \\mid \\theta)}{f(y_1, y_2)}  \n$$  \n\n代入条件独立性假设：  \n\n$$  \nf(\\theta \\mid y_1, y_2) = \\frac{f(\\theta) f(y_1 \\mid \\theta) f(y_2 \\mid \\theta)}{f(y_1) f(y_2)}  \n$$  \n\n这与顺序观察数据所得的后验分布相同：  \n\n$$  \nf(\\theta \\mid y_1, y_2) = \\frac{f(\\theta) L(\\theta \\mid y_1) L(\\theta \\mid y_2)}{f(y_1) f(y_2)}  \n$$  \n\n因此，不论是一次性观察所有数据，还是按顺序逐步观察数据，最终的后验分布是相同的。  \n"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"52BEDCE502104B189E5F9D92E545EBF4","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"**总结**：  \n\n1. 贝叶斯顺序分析的两大特性——**数据顺序不变性**和**累积数据依赖性**——可以通过以上数学证明得到验证。  \n    \n2. 无论数据是顺序观察还是一次性观察，或者数据顺序如何变化，最终的后验分布总是不变的。  \n   \n3. 这一特性使得贝叶斯分析在处理动态和实时数据时具有极大的灵活性和可靠性。  \n\n\nQ: 这里与我们在研究中所反对的“收集数据直到$p$值显著为止”的做法有什么区别？"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"EF995642A99743B7A01FCC186914A8A3","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"## 先验(prior)不同, 后验也不同"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"A1762214B7914E7B9CF0D2466A50C4E7","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"**不同先验分布对于后验的影响**  \n\n在随机点运动任务中，被试正确探测出点的运动方向的取值范围在0-1之间。  \n\n- 回顾上节课，根据(Shooshtari et al., 2019)的心理物理曲线，我们可以计算或预测当随机点的一致性为5%时，个体的正确率约为70%。  \n    \n- 而根据(Evans et al., 2020)的实验结果，我们发现其中一个被试在随机点的一致性为5%时，个体的正确率约为60%。  \n\n<center> <img src='https://cdn.kesci.com/upload/skeayxhg1s.png?imageView2/0/w/960/h/960'> </center>  \n\n> Shooshtari, S. V., Sadrabadi, J. E., Azizi, Z., & Ebrahimpour, R. (2019). Confidence representation of perceptual decision by EEG and eye data in a random dot motion task. Neuroscience, 406, 510–527. https://doi.org/10.1016/j.neuroscience.2019.03.031  \n> Evans, N. J., Hawkins, G. E., & Brown, S. D. (2020). The role of passing time in decision-making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(2), 316–326. https://doi.org/10.1037/xlm0000725 "},{"cell_type":"markdown","metadata":{"id":"B7B8EBA6D4A54FE19041C027CAD89FEA","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"本节课我们考虑如下三种不同的先验分布：  \n\n1. 第一种先验和上节课相同，认为参数值$\\pi$(正确率) 在 0.7 左右，用 Beta(70,30)表示。  \n   \n2. 第二种先验比较极端，认为参数值$\\pi$(正确率)大于 0.6，并且其值越大约有可能，用 Beta(10,1)表示。  \n   \n3. 最后一种“躺平”的思路认为，参数在$\\pi$ 0-1 之间出现的可能性是完全相同的，即先验可以用均匀分布表示 Beta(1,1)。"},{"cell_type":"code","metadata":{"_id":"1AFCD5492A58422D8F141B97E590339C","collapsed":false,"id":"CF9C55D0CB344A75A7B45FAF090BF0E7","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"skip"},"tags":[],"trusted":true},"source":"plot_pdf <- function(alpha, beta, level = 0.95,\n                     line_col = \"#008b92\", title = NULL,\n                     baseline = 0) {\n  x  <- seq(0, 1, length.out = 1000)\n  y  <- dbeta(x, alpha, beta)\n  df <- data.frame(x, y)\n  \n  lo_outer <- qbeta((1 - level)/2, alpha, beta)\n  hi_outer <- qbeta(1 - (1 - level)/2, alpha, beta)\n  lo_inner <- qbeta(0.25, alpha, beta)\n  hi_inner <- qbeta(0.75, alpha, beta)\n  \n  mu <- alpha/(alpha + beta)\n  \n  df$y_cut <- if (baseline > 0) ifelse(df$y < baseline, NA, df$y) else df$y\n  \n  ggplot(df, aes(x, y_cut)) +\n    geom_line(linewidth = 1.6, colour = line_col, lineend = \"round\", na.rm = TRUE) +\n    annotate(\"segment\", x = 0, xend = 1, y = baseline, yend = baseline,\n             linetype = \"dashed\", colour = \"grey60\") +\n    annotate(\"segment\", x = lo_outer, xend = hi_outer, y = baseline, yend = baseline,\n             linewidth = 2, colour = \"black\", lineend = \"round\") +\n    annotate(\"segment\", x = lo_inner, xend = hi_inner, y = baseline, yend = baseline,\n             linewidth = 4, colour = \"black\", lineend = \"round\") +\n    annotate(\"point\", x = mu, y = baseline, shape = 21, size = 4.2,\n             stroke = 1.1, fill = \"white\", colour = \"black\") +\n    coord_cartesian(xlim = c(0, 1), ylim = c(baseline, max(df$y) * 1.08)) +\n    labs(title = sprintf(\"Beta(alpha=%d, beta=%d)\", alpha, beta),\n         x = \"x\", y = \"Density\") +\n    theme(axis.text.y = element_blank(),\n          axis.ticks.y = element_blank(),\n          axis.title.y = element_blank(),\n          axis.text.x  = ggplot2::element_text(size = 12),\n          axis.title.x = element_blank())+\n    papaja::theme_apa() \n}","outputs":[],"execution_count":9},{"cell_type":"code","metadata":{"collapsed":false,"id":"15A3A71EC4454EEEA9505DAD07C5854A","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"fragment"},"tags":[],"trusted":true},"source":"p1 <- plot_pdf(70, 30)\np2 <- plot_pdf(10, 1)\np3 <- plot_pdf(1, 1)\np1 + p2 + p3","outputs":[{"output_type":"display_data","data":{"text/plain":"plot without 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4/Wj3IUytvzKdW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\nq9ZBc6Uh2tyXDL3JICXDefUZSY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src=\"https://cdn.kesci.com/upload/rt/15A3A71EC4454EEEA9505DAD07C5854A/t350nkofyc.svg\">"},"metadata":{"image/png":{"width":600,"height":300},"image/jpeg":{"width":600,"height":300},"image/svg+xml":{"width":600,"height":300,"isolated":true},"application/pdf":{"width":600,"height":300}}}],"execution_count":10},{"cell_type":"markdown","metadata":{"_id":"009B9470B47F45F08E68DDCEAE9311BE","id":"07AA5A2EC3AD42769326606997063F68","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"**不同类型的先验**  \n\n我们来回顾一下从先验中我们可以获得什么信息  \n\n* 在上图中，不同的先验，反映了研究者对正确率的不同信念(认为$\\pi$主要集中分布在哪里)  \n\n* 同时，先验分布的集中程度也反映了人们对某种信念的肯定程度  "},{"cell_type":"markdown","metadata":{"id":"AB98DA1ABCE7482CB82372BB01EC4325","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"比如，对于$Beta(10,1)$这个先验，$\\pi$的取值集中分布在0.6-1.0这种“高正确率区域”，说明研究者对研究的正确率的信念是很肯定的。  \n\n而对于$Beta(1,1)$这个先验，$\\pi$的取值均匀分布在0-1之间，研究者觉得$\\pi$取任何值的可能性都是一样的，换言之他们也不知道$\\pi$可能是多少。  \n\n以上两种先验，可被总结为**信息型先验(informative prior)** 和 **模糊型先验(vague prior)** ，其中：  \n\n**信息型先验**：  \n- 先验分布较窄，取值范围小。  \n  \n- 代表研究者对研究的正确率有强烈且确定的信念。  \n\n**模糊型先验**：  \n- 先验分布较宽，取值范围大。  \n  \n- 代表研究者对研究的正确率缺乏确定的信念。"},{"cell_type":"markdown","metadata":{"_id":"F74FFB5588C7402DBB6824EFB7714AE1","id":"2A8AF579269247FEA618F77FDB36687F","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"**结合数据查看先验对于后验的影响**  \n\n在继续探究不同的先验如何影响后验之前，我们还需要一些**数据**  \n\n我们依旧以Evans et al.（2020, Exp. 1） 的数据为例。  \n\n> Evans, N. J., Hawkins, G. E., & Brown, S. D. (2020). The role of passing time in decision-making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(2), 316–326. https://doi.org/10.1037/xlm0000725  "},{"cell_type":"code","metadata":{"_id":"E58D97ACDDCD43C19BE62EC1EC776E9A","collapsed":false,"id":"BC50E11F0A25439C9BFAAC6618E97F0E","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"data <- tryCatch({\n  read.csv('/home/mw/input/bayes3797/evans2020JExpPsycholLearn_exp1_full_data.csv') #平台路径\n}, error = function(e) {\n  read.csv('data/evans2020JExpPsycholLearn_exp1_full_data.csv') #本地路径\n})\n\ndata %>% \n  group_by(subject) %>% \n  summarise(mean_correct = mean(correct, na.rm = TRUE)) %>% \n  head(10) %>%\n  as.data.frame()\n\n# 选取需要的列\ndata <- data %>%\n  select(subject, percentCoherence, correct, RT)\n\n# 筛选符合条件的数据\ndata_subj1 <- data %>%\n  filter(subject == 82111, percentCoherence == 5)\nhead(data_subj1, 5)","outputs":[{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 10 × 2</caption>\n<thead>\n\t<tr><th scope=col>subject</th><th scope=col>mean_correct</th></tr>\n\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>31727</td><td>0.6513098</td></tr>\n\t<tr><td>65359</td><td>0.7533156</td></tr>\n\t<tr><td>66670</td><td>0.6808722</td></tr>\n\t<tr><td>71329</td><td>0.6970173</td></tr>\n\t<tr><td>71737</td><td>0.8225108</td></tr>\n\t<tr><td>75445</td><td>0.6686131</td></tr>\n\t<tr><td>77704</td><td>0.6115352</td></tr>\n\t<tr><td>77845</td><td>0.6754850</td></tr>\n\t<tr><td>79861</td><td>0.7652916</td></tr>\n\t<tr><td>80035</td><td>0.6675462</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 10 × 2\n\n| subject &lt;int&gt; | mean_correct &lt;dbl&gt; |\n|---|---|\n| 31727 | 0.6513098 |\n| 65359 | 0.7533156 |\n| 66670 | 0.6808722 |\n| 71329 | 0.6970173 |\n| 71737 | 0.8225108 |\n| 75445 | 0.6686131 |\n| 77704 | 0.6115352 |\n| 77845 | 0.6754850 |\n| 79861 | 0.7652916 |\n| 80035 | 0.6675462 |\n\n","text/latex":"A data.frame: 10 × 2\n\\begin{tabular}{ll}\n subject & mean\\_correct\\\\\n <int> & <dbl>\\\\\n\\hline\n\t 31727 & 0.6513098\\\\\n\t 65359 & 0.7533156\\\\\n\t 66670 & 0.6808722\\\\\n\t 71329 & 0.6970173\\\\\n\t 71737 & 0.8225108\\\\\n\t 75445 & 0.6686131\\\\\n\t 77704 & 0.6115352\\\\\n\t 77845 & 0.6754850\\\\\n\t 79861 & 0.7652916\\\\\n\t 80035 & 0.6675462\\\\\n\\end{tabular}\n","text/plain":"   subject mean_correct\n1  31727   0.6513098   \n2  65359   0.7533156   \n3  66670   0.6808722   \n4  71329   0.6970173   \n5  71737   0.8225108   \n6  75445   0.6686131   \n7  77704   0.6115352   \n8  77845   0.6754850   \n9  79861   0.7652916   \n10 80035   0.6675462   "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 5 × 4</caption>\n<thead>\n\t<tr><th></th><th scope=col>subject</th><th scope=col>percentCoherence</th><th scope=col>correct</th><th scope=col>RT</th></tr>\n\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>1</th><td>82111</td><td>5</td><td>1</td><td>1978</td></tr>\n\t<tr><th scope=row>2</th><td>82111</td><td>5</td><td>1</td><td>1492</td></tr>\n\t<tr><th scope=row>3</th><td>82111</td><td>5</td><td>1</td><td>1262</td></tr>\n\t<tr><th scope=row>4</th><td>82111</td><td>5</td><td>0</td><td>4318</td></tr>\n\t<tr><th scope=row>5</th><td>82111</td><td>5</td><td>0</td><td>4514</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 5 × 4\n\n| <!--/--> | subject &lt;int&gt; | percentCoherence &lt;int&gt; | correct &lt;int&gt; | RT &lt;int&gt; |\n|---|---|---|---|---|\n| 1 | 82111 | 5 | 1 | 1978 |\n| 2 | 82111 | 5 | 1 | 1492 |\n| 3 | 82111 | 5 | 1 | 1262 |\n| 4 | 82111 | 5 | 0 | 4318 |\n| 5 | 82111 | 5 | 0 | 4514 |\n\n","text/latex":"A data.frame: 5 × 4\n\\begin{tabular}{r|llll}\n  & subject & percentCoherence & correct & RT\\\\\n  & <int> & <int> & <int> & <int>\\\\\n\\hline\n\t1 & 82111 & 5 & 1 & 1978\\\\\n\t2 & 82111 & 5 & 1 & 1492\\\\\n\t3 & 82111 & 5 & 1 & 1262\\\\\n\t4 & 82111 & 5 & 0 & 4318\\\\\n\t5 & 82111 & 5 & 0 & 4514\\\\\n\\end{tabular}\n","text/plain":"  subject percentCoherence correct RT  \n1 82111   5                1       1978\n2 82111   5                1       1492\n3 82111   5                1       1262\n4 82111   5                0       4318\n5 82111   5                0       4514"},"metadata":{}}],"execution_count":11},{"cell_type":"markdown","metadata":{"_id":"3C824974CB554041880F6EFC0351E45C","id":"5EC7C904CBA34C95B137FF3518583E31","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"**🤔 思考时间**  \n\n下图画出了三种先验-似然组合，  \n\n我们可以猜测一下后验分布的形状？  \n"},{"cell_type":"code","metadata":{"_id":"9403921947EF40B59993B5E7E9C2CD8C","collapsed":false,"id":"106B6716ABBD447D9143B278714A97D7","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"# 分别创建三个图（不包含后验）\np1 <- bayesian_analysis_plot(70, 30, y = 152,  n = 254, plot_posterior = FALSE) +\nggtitle(sprintf(\"prior:Beta(alpha=%d, beta=%d)\", 70, 30)) + \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(6, \"pt\"),\n    legend.key.width  = grid::unit(8, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np2 <- bayesian_analysis_plot(10, 1, y = 152, n = 254, plot_posterior = FALSE) +\nggtitle(sprintf(\"prior:Beta(alpha=%d, beta=%d)\", 10, 1))+ \n  theme(    \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(6, \"pt\"),\n    legend.key.width  = grid::unit(8, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np3 <- bayesian_analysis_plot(1, 1, y = 152,  n = 254, plot_posterior = FALSE) +\nggtitle(sprintf(\"prior:Beta(alpha=%d, beta=%d)\", 1, 1))  + \n  theme(     \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(6, \"pt\"),\n    legend.key.width  = grid::unit(8, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np1 + p2 + p3 ","outputs":[{"output_type":"display_data","data":{"text/plain":"plot without title","application/pdf":"JVBERi0xLjcKJbXtrvsKNCAwIG9iago8PCAvTGVuZ3RoIDUgMCBSCiAgIC9GaWx0ZXIgL0Zs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87InWsKipVpZPnP5Va+4py6/4KgbIrGpILyA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\nUv8OZjS3swplbmRzdHJlYW0KZW5kb2JqCjE5IDAgb2JqCiAgIDE4MTAK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src=\"https://cdn.kesci.com/upload/rt/106B6716ABBD447D9143B278714A97D7/t3513b8vox.svg\">"},"metadata":{"image/png":{"width":600,"height":300},"image/jpeg":{"width":600,"height":300},"image/svg+xml":{"width":600,"height":300,"isolated":true},"application/pdf":{"width":600,"height":300}}}],"execution_count":16},{"cell_type":"markdown","metadata":{"_id":"9EF7F36C5E2B45FEA6C627915CE81881","id":"120F0C8F01544CEB8926866B8B1F3DA0","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"**后验图示**  \n\n我们可以使用公式来快速得到三种后验的表达式  \n$$  \n\\pi | (Y = y) \\sim \\text{Beta}(\\alpha + y, \\, \\beta + n - y)  \n$$  \n\n<center>  \n\n|Analyst|Prior  |Posterior  \n|----|-----|----|  \n|$\\alpha$   |Beta(70,30)|Beta(222,131)|  \n|$\\beta$   |Beta(10,1)|Beta(162,102)|  \n|mean   |Beta(1,1) |Beta(153,102)|  \n\n</center>  \n\t\n在表格中，每一行展示了不同被试的先验分布和后验分布。通过结合先验和观测数据，我们得到了相应的后验 Beta 分布。  \n\n接下来，我们可以将这些分布可视化，绘制出三种后验分布的图示，以便直观展示更新后的正确率 $\\pi$ 分布。  \n "},{"cell_type":"code","metadata":{"_id":"2647F2B7E94B4F81A323B506D5498E68","collapsed":false,"id":"DDF5F65A7B49420EB190CA3F8567F716","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"# 分别创建三个图（包含后验）\np1 <- bayesian_analysis_plot(70, 30, y = 152,  n = 254, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"prior:Beta(alpha=%d, beta=%d)\", 70, 30)) + \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(6, \"pt\"),\n    legend.key.width  = grid::unit(10, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np2 <- bayesian_analysis_plot(10, 1, y = 152, n = 254, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"prior:Beta(alpha=%d, beta=%d)\", 700, 300)) + \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(6, \"pt\"),\n    legend.key.width  = grid::unit(10, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np3 <- bayesian_analysis_plot(1, 1, y = 152,  n = 254, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"prior:Beta(alpha=%d, beta=%d)\", 7000, 3000)) + \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(6, \"pt\"),\n    legend.key.width  = grid::unit(10, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np1 + p2 + p3","outputs":[{"output_type":"display_data","data":{"text/plain":"plot without 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Wpyzif8oPQCkFd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\n2YGbtad+i+vGJNv2Hbh9S7GWqqCSYK7t5SIt1dqYduIerZu18RkE+q07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zesXeg7yYM7Lw9+YbDftpnnwakGtUmMzYxiz5Se9a3a9S/l+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\nOSAwIDUwMCAyMjIuMTY3OTY5IDgzMy4wMD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src=\"https://cdn.kesci.com/upload/rt/DDF5F65A7B49420EB190CA3F8567F716/t351905nvj.svg\">"},"metadata":{"image/png":{"width":600,"height":300},"image/jpeg":{"width":600,"height":300},"image/svg+xml":{"width":600,"height":300,"isolated":true},"application/pdf":{"width":600,"height":300}}}],"execution_count":19},{"cell_type":"markdown","metadata":{"id":"281C4B0AD18541FF891FAF7B529971A9","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"我们关注信息型先验和模糊型先验下的后验有什么区别：  \n\n1. **模糊型先验下**：  \n   - 如果**研究者对正确率没有明确预期**，后验分布几乎完全由数据（似然分布）决定。由于模糊型先验（如 $\\text{Beta}(1, 1)$）不提供关于 $\\pi$ 的有效信息，后验与似然分布几乎重合。  \n\n2. **信息型先验下**：  \n   - 当**研究者对正确率有较强的信念**，后验分布依然会受到先验的显著影响。例如，使用 $\\text{Beta}(10, 1)$ 作为信息型先验，后验分布会集中在 0.6 - 0.8 区域，反映了研究者对 $\\pi$ 的较高信心。  \n\n3. **总结**：  \n   - **模糊型先验**：后验主要由数据决定。  \n  \n   - **信息型先验**：先验对后验有强影响，后验分布倾向于维持先验的形状。"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"E4074EE2579248D89F6D98B5519DC8E5","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"### 极端先验"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"AA8A420E49A54CAA9CFB5AC178AD6A64","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"在贝叶斯分析中，虽然我们通常讨论有信息和无信息的先验，但有时先验选择可能会导致严重的偏离。极端固执的先验模型可能会使贝叶斯方法失去其顺序分析的优势。  \n\n- 这种极端先验模型通常包含先验概率为零的信念。  \n\n- 例如，研究者可能对随机点运动任务的正确率持有固执的观点，坚信其正确率较低。他们可能认为 $\\pi$ 的任何值在 0 到 0.25 之间都是等可能的，并坚决认为其不会超过 0.25。为了表达这种先验信念，可以采用 0 到 0.25 之间的均匀模型（uniform model）。  \n\n$$  \n\\pi \\sim \\text{Unif}(0,0.25)  \n$$  \n\n$$  \nf(\\pi) = 4 \\; \\text{ for } \\pi \\in [0, 0.25]  \n$$  \n\n现在，假设现在有人告诉实验者他做完实验后的正确率为80%，这个 80% 的数据与实验者的信念相悖。  \n\n❔问题！请根据这个数据猜测一下实验者对于随机点运动任务正确率的后验推断。  \n\n\n![](https://www.bayesrulesbook.com/bookdown_files/figure-html/ch4-stubborn-plot-1.png)"},{"cell_type":"code","metadata":{"collapsed":false,"id":"7A2F5C0945F44C44ACEA27BA726929FB","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"skip"},"tags":[],"trusted":true},"source":"# -------\n# ------","outputs":[],"execution_count":null},{"cell_type":"markdown","metadata":{"id":"375DAFCB921F4345B3433739A4AF6897","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"\n尽管图 (c)看起来奇怪， 但它的确代表了贝叶斯模型得到数据对$\\pi$的更新。  \n\n- **后验概率模型的定义与先验概率模型的定义相同**。  \n- 也就是说，后验模型的形状继承自先验模型。  \n- 由于实验者的先验信念不存在对于随机点运动任务的信任，也就是超过 0.25 的 $\\pi$ 值的概率分配为零，他们的后验模型也将随之把该范围内的所有值的概率分配为零。  \n- 从数学上讲，对于任意的 $\\pi \\notin [0, 0.25]$，后验概率密度函数 $f(\\pi∣y = 8) = 0$，对于任意的 $\\pi \\in [0, 0.25]$，有  \n\n$$  \n\\begin{split}  \nf(\\pi | y=8)  \n& \\propto f(\\pi)L(\\pi | y=8) \\\\  \n& = 4 \\cdot \\left(\\!\\begin{array}{c} 10 \\\\ 8 \\end{array}\\!\\right) \\ \\pi^{8} (1-\\pi)^{2} \\\\  \n& \\propto \\pi^{8} (1-\\pi)^{2}. \\\\  \n\\end{split}  \n$$  \n\n这个数学结果的含义是巨大的。  \n- 无论实验者收集到多少相反的证据，他的后验概率永远不会超过 0.25 的上限 （“ **** 限制了我的想象力”）"},{"cell_type":"markdown","metadata":{"id":"767AA425D50F428E94FE7CB492E6DAB0","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"此外，还有一种情况是，对于有信息的先验，如果过于偏向某一特定值，也可能导致极端先验的出现。  \n\n例如，同样对于先验是 0.7 的情况，如果先验过于有“信息”，那么数据可能对后验不产生或产生较小的影响。"},{"cell_type":"code","metadata":{"collapsed":false,"id":"FC6ACC68515F4D68A9EB8520284E4EF2","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"fragment"},"tags":[],"trusted":true},"source":"# 分别创建三个图（包含后验）\np1 <- bayesian_analysis_plot(70, 30, y = 152,  n = 254, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"prior:Beta(alpha=%d, beta=%d)\", 70, 30)) + \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(5, \"pt\"),\n    legend.key.width  = grid::unit(10, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np2 <- bayesian_analysis_plot(700, 300, y = 152, n = 254, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"prior:Beta(alpha=%d, beta=%d)\", 700, 300)) + \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(5, \"pt\"),\n    legend.key.width  = grid::unit(10, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np3 <- bayesian_analysis_plot(7000, 3000, y = 152,  n = 254, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"prior:Beta(alpha=%d, beta=%d)\", 7000, 3000)) + \n  theme(       \n    legend.text       = element_text(size = 10),\n    legend.key.height = grid::unit(5, \"pt\"),\n    legend.key.width  = grid::unit(10, \"pt\"),\n    legend.spacing.x  = grid::unit(4, \"pt\")\n  )\n\np1 + p2 + p3","outputs":[{"output_type":"display_data","data":{"text/plain":"plot without 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ZO7/mUHwRWCOxy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\nOD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src=\"https://cdn.kesci.com/upload/rt/FC6ACC68515F4D68A9EB8520284E4EF2/t351gz4m4c.svg\">"},"metadata":{"image/png":{"width":600,"height":300},"image/jpeg":{"width":600,"height":300},"image/svg+xml":{"width":600,"height":300,"isolated":true},"application/pdf":{"width":600,"height":300}}}],"execution_count":20},{"cell_type":"markdown","metadata":{"id":"440506030DFF4320AFCE62C2BDB26CBA","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"Tips：如何避免令人遗憾的先验模型  \n\t- 确保对每个可能的$\\pi$值都分配非零的可信度，即使这个可信度接近于零。例如，如果$\\pi$是一个可以从 0 到 1 的比例，那么你的先验模型也应该在这个连续范围内进行定义。  \n\n![Image Name](https://cdn.kesci.com/upload/image/rhqcb9gji7.png?imageView2/0/w/500/h/500) "},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"AEFA2C8E6843473187CDC5685954FA63","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"### 再看贝叶斯的主观性"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"0BF72E32C126460CADA636E7C5D08CFF","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"在之前我们提到了一个关于贝叶斯统计的常见批评观点——它的主观性。  \n  \n- 一些人担心“主观地”调整先验模型会使贝叶斯分析人员得出他们想要的任何结论。  \n- 在学习完这节课后我们可以更严谨地回应这个观点。  \n\n> 在我们回应之前，重新思考并扩展一下你在整本书中探讨过的一些概念。  \n> ❔问题！对于下面的每个陈述，请判断该陈述是真还是假，并提供你的推理。  \n\n- 所有的先验选择都是具有信息量的。  \n- 有可能有充分的理由选择具有信息量的先验。  \n- 任何先验选择都可以被足够多的数据克服。  \n- 频率学派的范式是完全客观的。"},{"cell_type":"markdown","metadata":{"id":"7176C8837010433A9EC5553F21FE6D9A","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"答案：  \n\n1. 错误。模糊的先验通常是不具有信息量的。  \n2. 正确。我们可能有充足的先前数据或专业知识来构建我们的先验。  \n3. 错误。如果你将潜在的参数值赋予零先验概率，任何数量的数据都无法改变它！  \n4. 错误。主观性总是渗透到频率学派和贝叶斯分析中。在贝叶斯范式中，我们至少可以命名和量化这种主观性的方面。"},{"cell_type":"markdown","metadata":{"id":"3351DAC0D0514163AF461A2739290761","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"🔍回顾：贝叶斯学派和频率学派的差异对比  \n\n**任何统计分析方法都不可能完全客观，因此主观性是一个相对概念:**  \n\n* 贝叶斯学派的主观性通过先验的设定来体现，透明，不易让人产生误解  \n\n* 频率学派的主观性暗含在各种**前提预设**中，比如方差分析中的方差齐性和正态性，这种看似‘客观的’预设，一方面难以满足，一方面也是一种主观的设定。  \n\n* 更为宏观的来说，样本的抽取，数据清理方式的选择，分析方法的选择，$p$值的设定，这些都存在主观性。因此，频率学派并没有想象的那么‘客观’。  \n\n* 主观不一定是坏事：通过量化方法将个体的经验和专家知识整合到数据分析之中。  \n\n\n| **频率学派**                                           | **贝叶斯学派**                                         |  \n|-------------------------------------------------------|-------------------------------------------------------|  \n| **概率定义**：概率是事件在无限重复试验中的频率          | **概率定义**：概率是对假设的信念度量                   |  \n| **假设**：假设是固定的，数据是随机的                    | **假设**：假设是随机的，数据是固定的                   |  \n| **推断方式**：基于假设检验，通过$p$值判断是否拒绝零假设    | **推断方式**：通过更新先验与新数据计算后验概率         |  \n| **置信区间**：在重复试验中，95%的区间包含真实参数         | **可信区间**：给出某参数位于区间内的概率（如95%可信度） |  \n| **$p$值**：衡量在零假设下，观测数据或更极端数据的概率      | **后验概率**：给出假设为真的更新概率                   |  \n| **数据独立性**：推断只基于当前试验数据，不考虑先验信息    | **先验信息**：结合历史数据或专家意见，用于更新推断     |  \n| **实验重复性假设**：推断基于实验的假想重复性              | **逐步积累信息**：通过结合新数据不断更新和完善假设     |  \n| **适应性**：实验设计固定，不能在中途更新或调整             | **适应性**：可以灵活调整试验设计和决策，如自适应试验   |  \n\n来源：  \n> Goligher, E. C., Heath, A., & Harhay, M. O. (2024). Bayesian statistics for clinical research. *The Lancet, 404*(10457), 1067-1076."},{"cell_type":"markdown","metadata":{"id":"F3B0C499473249E78617D62A3D4D8625","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"**总结**：贝叶斯分析确实可以基于“主观”经验建立先验。  \n- 这不定是件坏事，先验可以反映出丰富的过去经验，这些经验应该纳入我们的分析中。相反，如果不这么做对我们的数据分析来说是很大的损失。即使主观先验与实际观察到的证据相矛盾，随着证据的累积，先验的影响会逐渐消失。  \n  \n- 极端的先验不受到数据影响，但这种情况是避免的。如果主观先验非常地顽固和极端，它会将可能的参数值的概率分配为零，那么任何数量的反证据都不足以改变它。"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"3A10EF977B8C4AC2A99614F10BDBAA41","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"## 贝叶斯推断：先验与数据的平衡"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"C9E6C8C85A564574A9C2589AA597EB93","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"**不同先验+不同似然产生的后验分布**  \n\n**--> 后验分布是两者间的平衡。**  \n\n我们可以将不同的先验和似然组合在一起，观察后验的变化"},{"cell_type":"code","metadata":{"collapsed":false,"id":"EA8CFEDCD0974BC882DE3B4916811F3D","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"# 分别创建九个图（包含后验）\np1 <- bayesian_analysis_plot(70, 30, y = 77,  n = 128, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"Beta(alpha=%d, beta=%d)\", 70, 30)) + \n  theme(       \n    legend.text       = element_text(size = 7),\n    legend.key.height = grid::unit(3, \"pt\"),\n    legend.key.width  = grid::unit(3, \"pt\"),\n    legend.spacing.x  = grid::unit(3, \"pt\")\n  ) \n\np2 <- bayesian_analysis_plot(70, 30, y = 152, n = 254, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"Beta(alpha=%d, beta=%d)\", 70, 30)) + \n  theme(       \n    legend.text       = element_text(size = 7),\n    legend.key.height = grid::unit(3, \"pt\"),\n    legend.key.width  = grid::unit(3, \"pt\"),\n    legend.spacing.x  = grid::unit(3, \"pt\")\n  ) \np3 <- bayesian_analysis_plot(70, 30, y = 231,  n = 385, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"Beta(alpha=%d, beta=%d)\", 70, 30)) + \n  theme(       \n    legend.text       = element_text(size = 7),\n    legend.key.height = grid::unit(3, \"pt\"),\n    legend.key.width  = grid::unit(3, \"pt\"),\n    legend.spacing.x  = grid::unit(3, \"pt\")\n  ) \n\np4 <- bayesian_analysis_plot(10, 1, y = 77,  n = 128, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"Beta(alpha=%d, beta=%d)\", 10, 1)) + \n  theme(       \n    legend.text       = element_text(size = 7),\n    legend.key.height = grid::unit(3, \"pt\"),\n    legend.key.width  = grid::unit(3, \"pt\"),\n    legend.spacing.x  = grid::unit(3, \"pt\")\n  ) \n\np5 <- bayesian_analysis_plot(10, 1, y = 152, n = 254, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"Beta(alpha=%d, beta=%d)\", 10, 1)) + \n  theme(       \n    legend.text       = element_text(size = 7),\n    legend.key.height = grid::unit(3, \"pt\"),\n    legend.key.width  = grid::unit(3, \"pt\"),\n    legend.spacing.x  = grid::unit(3, \"pt\")\n  ) \n\np6 <- bayesian_analysis_plot(10, 1, y = 231,  n = 385, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"Beta(alpha=%d, beta=%d)\", 10, 1)) + \n  theme(       \n    legend.text       = element_text(size = 7),\n    legend.key.height = grid::unit(3, \"pt\"),\n    legend.key.width  = grid::unit(3, \"pt\"),\n    legend.spacing.x  = grid::unit(3, \"pt\")\n  ) \n\np7 <- bayesian_analysis_plot(1, 1, y = 77,  n = 128, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"Beta(alpha=%d, beta=%d)\", 1, 1)) + \n  theme(       \n    legend.text       = element_text(size = 7),\n    legend.key.height = grid::unit(3, \"pt\"),\n    legend.key.width  = grid::unit(3, \"pt\"),\n    legend.spacing.x  = grid::unit(3, \"pt\")\n  ) \n\np8 <- bayesian_analysis_plot(1, 1, y = 152, n = 254, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"Beta(alpha=%d, beta=%d)\", 1, 1)) + \n  theme(       \n    legend.text       = element_text(size = 7),\n    legend.key.height = grid::unit(3, \"pt\"),\n    legend.key.width  = grid::unit(3, \"pt\"),\n    legend.spacing.x  = grid::unit(3, \"pt\")\n  ) \np9 <- bayesian_analysis_plot(1, 1, y = 231,  n = 385, plot_posterior = TRUE) +\n  ggtitle(sprintf(\"Beta(alpha=%d, beta=%d)\", 1, 1)) + \n  theme(       \n    legend.text       = element_text(size = 7),\n    legend.key.height = grid::unit(3, \"pt\"),\n    legend.key.width  = grid::unit(3, \"pt\"),\n    legend.spacing.x  = grid::unit(3, \"pt\")\n  ) \n\np1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9\n","outputs":[{"data":{"image/png":"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size 1800x1500 with 9 Axes>"]},"metadata":{},"output_type":"display_data"}],"execution_count":null},{"cell_type":"markdown","metadata":{"id":"11185790731D4AC4A9F696B3107F237A","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"* 从左往右，数据的试次从128增加到385，似然的分布越集中，对后验的影响也越来越大  \n\n* 从上往下，先验分布从信息型(informative prior)变为模糊型(vague prior)，先验分布对后验分布的影响也就越来越小  \n\n* 而最后一列告诉我们，无论不同的人对先验的差异有多大，只要似然的分布够集中，即数据提供的信息足够丰富，那么后验分布主要受到来自数据的影响，不同人的后验分布也并不会相差太大。"},{"cell_type":"markdown","metadata":{"id":"F2A58BD4D741451F946D697C48E9B1F2","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":"我们再次看到后验是先验模型和数据的平衡。总的来说，会观察到以下趋势：  \n\n- 先验影响：先验越不模糊、越具有信息量，即我们对先验越有确定性，先验对后验的影响就越大。  \n  \n- 数据影响：我们拥有的数据越多，数据对后验的影响就越大。因此，即使具有不同的先验，如果两次测量的数据充足并且相似，那么他们的后验结果会非常相似。  \n \n- 此外，在顺序贝叶斯分析中，随着数据越来越多，后验逐渐更新。这个后验的最终结果不受观察数据的顺序（即后验对数据的顺序不变）。"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"F7991B704B984E418AC031BC150AD048","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"## 代码练习  \n\n练习 4.1（将先验与描述配对）。  \n\n下面列出了五种不同的π的先验模型。  \n请用以下描述之一标记每个先验：有些偏向 $\\pi$ < 0.5，有些强烈偏向$\\pi$ < 0.5，有些将$\\pi$置于0.5中心，有些偏向$\\pi$ > 0.5，有些强烈偏向$\\pi$ > 0.5。  \n- a) Beta(1.8,1.8)  \n- b) Beta(3,2)  \n- c) Beta(1,10)  \n- d) Beta(1,3)  \n- e) Beta(17,2)  \n\n练习 4.2（将图形与代码配对）。  \n\n下面的绘图函数更可能使用了哪些参数生成了下面的图形？  \n![](https://www.bayesrulesbook.com/bookdown_files/figure-html/unnamed-chunk-142-1.png)  \n- a) $\\alpha$ = 2, $\\beta$ = 2, y = 8, n = 11  \n- b) $\\alpha$ = 2, $\\beta$ = 2, y = 3, n = 11  \n- c) $\\alpha$ = 3, $\\beta$ = 8, y = 2, n = 6  \n- d) $\\alpha$ = 3, $\\beta$ = 8, y = 4, n = 6  \n- e) $\\alpha$ = 3, $\\beta$ = 8, y = 2, n = 4  \n- f) $\\alpha$ = 8, $\\beta$ = 3, y = 2, n = 4"},{"cell_type":"code","metadata":{"jupyter":{},"collapsed":false,"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"25EB306358FB4943A3F3C7662EFBA532","notebookId":"68d5045de81133f9301bdd7b","trusted":true},"source":"bayesian_analysis_plot <- function(\n    alpha, beta, y, n,\n    plot_prior      = TRUE,\n    plot_likelihood = TRUE,\n    plot_posterior  = TRUE,\n    xlabel          = expression(\"ACC\" ~ pi),\n    show_legend     = TRUE\n) {\n  \n  # 图例位置\n  legend_loc <- c(0.005, 0.995)  # 左上角\n  \n  # 先验分布\n  if (plot_prior) {\n    x_prior <- seq(qbeta(1e-4, alpha, beta), qbeta(1 - 1e-4, alpha, beta), length.out = 100)\n    y_prior <- dbeta(x_prior, alpha, beta)\n    prior_df <- data.frame(x = x_prior, y = y_prior, dist = \"Prior\")\n  }\n  \n  # 似然分布\n  if (plot_likelihood) {\n    x_like <- seq(0, 1, length.out = 1000)\n    y_like <- dbeta(x_like, y + 1, n - y + 1)\n    like_df <- data.frame(x = x_like, y = y_like, dist = \"Likelihood\")\n  }\n  \n  # 后验分布\n  if (plot_posterior) {\n    a_post <- alpha + y\n    b_post <- beta + n - y\n    x_post <- seq(qbeta(1e-4, a_post, b_post), qbeta(1 - 1e-4, a_post, b_post), length.out = 100)\n    y_post <- dbeta(x_post, a_post, b_post)\n    post_df <- data.frame(x = x_post, y = y_post, dist = \"Posterior\")\n  }\n  \n  # 合并数据 \n  plot_data <- do.call(rbind, Filter(Negate(is.null), list(\n    if (plot_prior)      transform(prior_df, dist = \"prior\")      else NULL,\n    if (plot_likelihood) transform(like_df,  dist = \"likelihood\") else NULL,\n    if (plot_posterior)  transform(post_df,  dist = \"posterior\")  else NULL\n  )))\n  plot_data$dist <- factor(plot_data$dist, c(\"prior\",\"likelihood\",\"posterior\"))\n  \n  # 作图\n  cols_fill <- c(prior=\"#f0e442\", likelihood=\"#0071b2\", posterior=\"#009e74\")\n  present   <- intersect(c(\"prior\",\"likelihood\",\"posterior\"), unique(plot_data$dist))\n  legend_levels <- as.vector(rbind(paste0(present, \"_line\"),\n                                   paste0(present, \"_fill\")))\n\n  labs_all <- setNames(c(\n    sprintf(\"Beta(alpha=%d, beta=%d)\", alpha, beta),          \"Prior\",\n    sprintf(\"Binomial(n=%d, p=%.2g)\", n, y/n),                \"Likelihood\",\n    sprintf(\"Beta(alpha=%d, beta=%d)\", alpha+y, beta+n-y),    \"Posterior\"\n  ), c(\"prior_line\",\"prior_fill\",\"likelihood_line\",\"likelihood_fill\",\n       \"posterior_line\",\"posterior_fill\"))[legend_levels]\n\n  plot_data$legend_line <- factor(paste0(plot_data$dist, \"_line\"), levels = legend_levels)\n  plot_data$legend_fill <- factor(paste0(plot_data$dist, \"_fill\"), levels = legend_levels)\n\n  p <- ggplot2::ggplot(plot_data, ggplot2::aes(x, y)) +\n    ggplot2::geom_area(ggplot2::aes(fill  = legend_fill),\n                       alpha = 0.7, position = \"identity\", colour = NA) +\n    ggplot2::geom_line(ggplot2::aes(color = legend_line), linewidth = 1.2, lineend = \"round\", linejoin = \"round\") +\n    ggplot2::labs(x = xlabel, y = \"Density\") +\n    ggplot2::scale_y_continuous(expand = c(0, 0)) +\n    \n    ggplot2::scale_fill_manual(\n      name   = NULL,\n      values = setNames(ifelse(grepl(\"_fill$\", legend_levels),\n                               cols_fill[sub(\"_fill$\", \"\", legend_levels)], NA),\n                        legend_levels),\n      breaks = legend_levels,\n      labels = labs_all,\n      drop   = FALSE\n    ) +\n    ggplot2::scale_color_manual(\n      name   = NULL,\n      values = setNames(ifelse(grepl(\"_line$\", legend_levels), \"black\", NA),\n                        legend_levels),\n      breaks = legend_levels,\n      labels = labs_all,\n      drop   = FALSE\n    ) +\n    ggplot2::guides(\n      fill  = ggplot2::guide_legend(order = 1, byrow = TRUE),\n      color = ggplot2::guide_legend(order = 1, byrow = TRUE)\n    ) +\n    papaja::theme_apa()+ \n    ggplot2::theme(\n      legend.margin = margin(t = 2, r = 4, b = 2, l = 4, unit = \"pt\"),\n      legend.background    = element_rect(fill = \"transparent\", colour = \"NA\", linewidth = 0.6),\n      legend.box.background= element_rect(fill = \"transparent\", colour = \"grey\", linewidth = 0.6),\n      legend.key           = element_rect(fill = \"transparent\", colour = NA),\n      axis.text.y  = ggplot2::element_blank(),\n      axis.ticks.y = ggplot2::element_blank(),\n      axis.title.y = ggplot2::element_blank(),\n      axis.text.x  = ggplot2::element_text(size = 12), \n      legend.box = \"vertical\",\n      legend.position      = legend_loc ,\n      legend.justification = c(0, 1) \n    )\n  \n  return(p)\n}","outputs":[],"execution_count":null},{"cell_type":"code","metadata":{"collapsed":false,"id":"3C0BAA8C61D44C018A4175154E0171F6","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"#---------------------------------------------------------------------------\n#                            请替换...填入 Beta 分布参数, alpha 和 beta\n#---------------------------------------------------------------------------\n# 设置 Beta 分布参数\nalpha = ...     # alpha\nbeta  = ...     # beta\n\n#---------------------------------------------------------------------------\n#                            请替换...请填入观测数据 y 和 n\n#---------------------------------------------------------------------------\ny = ...     # y 代表支持数\nn = ...     # n 代表总人数\n\n#---------------------------------------------------------------------------\n#                            请使用 bayesian_analysis_plot 进行绘图\n#---------------------------------------------------------------------------\nbayesian_analysis_plot(...)","outputs":[],"execution_count":null},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"CD0DC3FB56BE49AC82102ABA14D908C7","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"经过尝试不同的参数和数据配置后，请进行思考🤔：  \n- 如何设置可以得到弱信息先验？  \n- 什么数据条件下，似然对于后验的影响最大。"},{"cell_type":"code","metadata":{"collapsed":false,"id":"2C8A30DC4CCE4152ADE2C51B3D2B23E6","jupyter":{},"notebookId":"68d5045de81133f9301bdd7b","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"# 答案\n\n#---------------------------------------------------------------------------\n#                            请填入 Beta 分布参数，alpha 和 beta\n#---------------------------------------------------------------------------\n# 设置 Beta 分布参数\nalpha = 10     # alpha\nbeta  = 50     # beta\n\n#---------------------------------------------------------------------------\n#                            请填入观测数据 y 和 n\n#---------------------------------------------------------------------------\ny = 80     # y 代表支持数\nn = 180    # n 代表总人数\n\nbayesian_analysis_plot(alpha, beta, y, n)","outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x400 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"execution_count":null},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"9ECDD0A3B1AF413BB44496C4563E3D6C","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"### 💐Bonus：使用数学公式证明，后验确实利用了来自先验和似然的信息"},{"cell_type":"markdown","metadata":{"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"},"id":"8B70C51184834CB687B141DC356F25F0","runtime":{"status":"default","execution_status":null,"is_visible":false},"notebookId":"68d5045de81133f9301bdd7b"},"source":"（* 注：以下涉及的公式包含上一节bonus中的内容）  \n\n由于后验分布属于beta分布，因此它的平均值可以写成：  \n\n$$  \nE(\\pi | Y=y)  = \\frac{\\alpha + y}{\\alpha + \\beta + n}  .  \n$$  \n\n我们可以将$\\frac{\\alpha + y}{\\alpha + \\beta + n}$拆成以下两部分  \n\n$$  \nE(\\pi | Y=y)  \n= \\frac{\\alpha}{\\alpha + \\beta + n} + \\frac{y}{\\alpha + \\beta + n}  \\\\  \n= \\frac{\\alpha}{\\alpha + \\beta + n}\\cdot\\frac{\\alpha + \\beta}{\\alpha + \\beta} + \\frac{y}{\\alpha + \\beta + n}\\cdot\\frac{n}{n}  \\\\  \n= \\frac{\\alpha + \\beta}{\\alpha + \\beta + n}\\cdot\\frac{\\alpha}{\\alpha + \\beta} + \\frac{n}{\\alpha + \\beta + n}\\cdot\\frac{y}{n}  \\\\  \n= \\frac{\\alpha + \\beta}{\\alpha + \\beta + n}\\cdot E(\\pi) + \\frac{n}{\\alpha + \\beta + n}\\cdot\\frac{y}{n}  .  \\\\  \n$$  \n\n$$  \ny: success \\space trial,\\; n: total \\space trials \\\\  \ny/n表示观察到的数据比例  \n$$  \n\n且，  \n$$  \n\\frac{\\alpha + \\beta}{\\alpha + \\beta + n} + \\frac{n}{\\alpha + \\beta + n} = 1.  \n\n$$  \n\n\n可以看到，后验均值可以被分解为  \n<center>  \n权重*先验均值 + 权重*数据  \n</center>  \n\n那么，当数据越多，即$n$越大时，先验的权重就会更小，接近0；而数据的权重就会越大  \n\n因此，后验正是权衡了来自先验和似然的信息"},{"cell_type":"markdown","metadata":{"id":"6B03B120CB864F5BA15B2FF77558271C","notebookId":"68d5045de81133f9301bdd7b","runtime":{"status":"default","execution_status":null,"is_visible":false},"jupyter":{},"scrolled":false,"tags":[],"slideshow":{"slide_type":"slide"}},"source":"附录：  \n\n本课的Python代码与R代码为单独的代码脚本，见：https://gitee.com/hcp4715/PyBayesian"}],"metadata":{"kernelspec":{"language":"R","display_name":"R","name":"ir"},"language_info":{"codemirror_mode":"r","name":"R","mimetype":"text/x-r-source","file_extension":".r","version":"3.3.2","pygments_lexer":"r"}},"nbformat":4,"nbformat_minor":5}